American Women Marathoners

Author

Gaston Sanchez

Published

April 30, 2025

1 Description

The data visualization of this module is based on the scatter plot that appears in the following New York Times article by Talya Minsberg and Kevin Quealy

Why Are American Women Running Faster Than Ever? We Asked Them—Hundreds of Them

https://www.nytimes.com/interactive/2020/02/28/sports/womens-olympic-marathon-trials.html

50 fastest U.S. women’s marathons per year

1.1 Details (datasheet)

Topic(s):
- Sports
- Athletics
- Women
- Marathon
- U.S.
Data:
- Size: medium data
- Format: data in HTML tables
- Requires web scraping
- Requires some cleansing (e.g. regex)
- Requires merging data
Graphic:
- Type: scatter plot
- Styles: ggplot, interactive ggiraph, interactive leaflet
- Interactive: no

1.2 R Packages

library(tidyverse) # ecosystem of data science packages
library(rvest)     # for web scraping

2 Data

The source of the data is the website World Athletics. The data collection involves web crawling and scraping several pages from this website. All the R code used for this task is in the script web-scraping-world-athletrics.R, located in the folder data/ of this repository.

In this data/ folder, you’ll find various HTML files of the form marathon-women-yyyy.html where yyyy indicates a given year from 2001 to 2024.

2.1 Data Preparation

The commands below import the HTML files in R, scrape the HTML tables into data frames, and extract the Mark and Competitor columns. The main output is a tidy table with columns:

Year: year
Mark: Time to complete marathon (string)
Competitor: Name of runner
hours: number of hours (from Mark)
mins: number of minutes (from Mark)
secs: number of seconds (from Mark)
time: total time in minutes to complete marathon
pace: time (minutes) to run a mile

# output vectors
Year = NULL
Mark = NULL
Competitor = NULL

# import HTML files, and extract columns Mark and Competitor from tables
for (y in 2001:2024) {
  tbls = read_html(paste0("data/marathon-women-", y, ".html")) |> 
    html_table()
  tbl_raw = tbls[[1]]
  Year = c(Year, rep(y, nrow(tbl_raw)))
  Mark = c(Mark, tbl_raw$Mark)
  Competitor = c(Competitor, tbl_raw$Competitor)
}

# assemble data
dat = tibble(
  Year = Year,
  Mark = Mark,
  Competitor = Competitor
)

# a bit of cleaning
dat = dat |> 
  mutate(
    hours = as.numeric(str_extract(Mark, "\\d")) * 60,
    mins = as.numeric(str_extract(Mark, "\\d{2}")),
    secs = as.numeric(str_extract(Mark, "\\d{2}$")) / 60,
    time = hours + mins + secs,
    pace = time / 26.2188 # marathon in miles
  )

head(dat)

# A tibble: 6 × 8
   Year Mark    Competitor            hours  mins  secs  time  pace
  <int> <chr>   <chr>                 <dbl> <dbl> <dbl> <dbl> <dbl>
1  2001 2:37:57 Magdalena LEWY-BOULET   120    37 0.95   158.  6.02
2  2001 2:39:20 Jill GAITENBY           120    39 0.333  159.  6.08
3  2001 2:40:33 Mary KNISELY            120    40 0.55   161.  6.12
4  2001 2:40:34 Michelle SIMONAITIS     120    40 0.567  161.  6.12
5  2001 2:40:46 Linda SOMERS-SMITH      120    40 0.767  161.  6.13
6  2001 2:41:41 Mary ELLIS              120    41 0.683  162.  6.17

3 Graphics

We go over a series of plots to work out the plotting details.

3.1 Graphic 1

First we do a sanity check:

ggplot(data = dat, aes(x = Year, y = time)) +
  geom_point() +
  labs(title = "50 fastest U.S. women’s marathons per year",
       x = "")

3.2 Graphic 2

We specify the point shape to 21 so that we can use colors for fill and border (color)

ggplot(data = dat, aes(x = Year, y = time)) +
  geom_point(fill = "tomato", shape = 21, color = "white") +
  labs(title = "50 fastest U.S. women’s marathons per year",
       x = "")

3.3 Graphic 3

We need to reverse the y-axis, this can be done with scale_y_everse(); also we use geom_jitter() to scatter the points along the x-axis

ggplot(data = dat, aes(x = Year, y = pace)) +
  geom_jitter(fill = "#eb2f72", shape = 21, color = "white", width = 0.1) +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(2001, 2024, by = 2)) +
  labs(title = "50 fastest U.S. women’s marathons per year",
       x = "")

3.4 Graphic 4

Finally, we take case of the rest of details: e.g. grid lines, background, scales, etc.

ggplot(data = dat, aes(x = Year, y = pace)) +
  geom_jitter(fill = "#eb2f72", shape = 21, color = "white", width = 0.1) +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(2001, 2024, by = 2)) +
  labs(title = "50 fastest U.S. women’s marathons per year",
       x = "") +
  theme(panel.grid.minor = element_blank(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line(color = "gray92"),
        panel.background = element_rect(fill = "gray97"))